code: def range_based_input_values():
      values = {
          "y_true": [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0],
          "y_pred": [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0],
          # Expected precision values with different positional biases
          "expected_range_based_precision_flat_positional_bias": 0.938,
          "expected_range_based_precision_front_positional_bias": 0.9,
          "expected_range_based_precision_middle_positional_bias": 0.958,
          "expected_range_based_precision_back_positional_bias": 0.975,
          # Expected recall values with different positional biases
          "expected_range_based_recall_flat_positional_bias": 0.817,
          "expected_range_based_recall_front_positional_bias": 0.908,
          "expected_range_based_recall_middle_positional_bias": 0.854,
          "expected_range_based_recall_back_positional_bias": 0.725,
      }
  
      return values
  
imports: import numpy
  import pandas
  import pytest
  from greykite.detection.common.ad_evaluation import confusion_matrix
  from greykite.detection.common.ad_evaluation import f1_score
  from greykite.detection.common.ad_evaluation import informedness_statistic
  from greykite.detection.common.ad_evaluation import matthews_corrcoef
  from greykite.detection.common.ad_evaluation import precision_score
  from greykite.detection.common.ad_evaluation import range_based_precision_score
  from greykite.detection.common.ad_evaluation import range_based_recall_score
  from greykite.detection.common.ad_evaluation import recall_score
  from greykite.detection.common.ad_evaluation import soft_f1_score
  from greykite.detection.common.ad_evaluation import soft_precision_score
  from greykite.detection.common.ad_evaluation import soft_recall_score
label_desc: Use image node_id 3 for calling a global function with example usage: range_based_input_values() and returns: values